Seaborn Heatmap Size


Seaborn is a data visualization Python package built on the matplotlib library. It gives you a way to represent the data in a statistical graphical form that is both relevant and appealing. One of the features offered by Seaborn is a heatmap, which uses a color palette to depict variation in linked data. In the Seaborn module, we may use the seaborn.heatmap() method to make heatmap charts.

Annotations are lines of text that appear on a heatmap cell to describe what a particular cell represents. The font size of the annotations is set by default, although it can be altered using the annot kws parameter of the heatmap() method. The annot kws is a dictionary-type option that requires a value for the size key. The size of the annotations is determined by the value assigned to this key. However, some conditions must be followed to raise the size of the annotations, like the heatmap() function’s annot parameter must be set to True, and the required size for the annot kws option must be set.

Syntax of the Heatmap in Seaborn

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seaborn.heatmap(data,  vmin=None, vmax=None,  annot=None,annot_kws=True, linewidths=0,cbar=None, cbar_kws=None,square=False, xticklabels=‘automóvil’, yticklabels=‘automóvil’, mask=None, ax=None, kwargs)

Data: Compel a 2D dataset into an ndarray. The index/column information from a Pandas DataFrame will be used to name the columns and rows.

vmin, vmax: Values will be used to anchor the colormap; otherwise, they will be deducted from the dataset and other term inputs.

annot: If True, fill each cell with the data value. Use it to annotate the heatmap rather than the data if it’s an array-like object with the same format as data. DataFrames will be matched based on location rather than index.

fmt: When adding annotations, use this string formatting code.

annot_kws: When the annot is True, the keyword parameters are passed to the  matplotlib.axes.Axes.text().

linewidths: The distance between the lines that should split each cell.

cbar: A bool parameter decides if a colorbar should be drawn.

cbar_ax: Axes from which to create the colorbar; otherwise, the space on the main axes will be taken up.

square: Adjust the axes attribute to “equal” if True so that each cell gets square-shaped.

xticklabels, yticklabels: Graph the data frame’s column names if True. If this is False, the column names should not be plotted. If the alternate labels are xticklabels, plot them as a list. Use the field names if the number is an integer, but only plot the first in labels. If you’re using “automóvil”, try to plot non-overlapping labels as densely as possible.

mask: Data will not be displayed in cells when the mask is True if this parameter is set to True. Masked cells are those that have missing values.

ax: Axes on which to build the plot; otherwise, use the currently active axes.

kwargs: Matplotlib.axes.Axes.pcolormesh() is passed to all other keyword parameters.

Example 1

The set() function establishes the configuration and theme of the Seaborn plots. The size of the plot can be identified with the RC option. We’ve defined the modules we’ll be utilizing in the Python script in the following example. After this, we have created data inside a variable Marks and called the data frame function. The data frame function has four student columns where we have recorded the marks students gained. We had set the data for the plot.

Now, the set function is defined where the size of the plot is mentioned in the figsize. Then, the Seaborn heatmap function is invoked where the corr function is applied on the Marks. The corr function returned all of the data frame’s columns that have a pairwise correlation.

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import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

marks = pd.DataFrame({«Student 1”: [6,3,1,7,3,10,5,4],

«Student 2» : [3,7,2,1,8,2,4,2],

Student3″ : [1,6,9,8,6,4,9,3],

«student 4» : [5,5,1,9,4,7,8,3]})

sns.set(rc = {‘figure.figsize’:(10,5)})

sns .heatmap(Marks.corr())

plt.show()

The heatmap plot is rendered with the specified figure size as follows:

In Python, the figure() method is used to begin or modify the current figure. In this diagram, the heatmap is shown. The function’s figsize parameter can be utilized to change the size. We must create data for generating the plot with the specified figure size. We have a data frame of four columns List1, List2, List3, and List4 and inserted random values in them. Then, we have a figure() method inside which we have defined the figure size. In the last step, the corr method is applied to the data frame using the heatmap function.

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import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

List = pd.DataFrame({«List 1”: [5,8,9,5,1,7,8,9],

«List 2» : [4,6,5,8,6,1,2,3],

“List3″ : [1,2,8,4,3,9,5,2],

«List4» : [7,1,5,6,3,10,5,8]})

plt.figure(figsize = (15,7))

sns.heatmap(List.corr())

plt.show()

The size is visualized in the subsequent figure of the heatmap plot.

Example 3

Here, we use the annot and annot_kws parameters for the heatmap size. We have loaded the sample dataset “tips” in the Seaborn load_dataset option, which is stored in the variable data. Then, we have called the heatmap function and provided the corr function for the dataset. Then, we have provided the annot option and set it as true. The annot_kws option is set with the size 12.

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import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset(«tips”)

sns-heatmap(data.corr(), annot=True, annot_kws={‘size’: 12})

plt.show()

The previous implementation compiles the following heatmap plot size:

Example 4

When it comes to determining the size, consideration must be used. When you provide a huge number, the annotations will be magnified far too much, making them impossible to read and interpret. They may even collapse over each other. Thus, rendering the heatmap unusable. We have chosen the data frame iris and loaded it inside the load_dataset function. Call the heatmap function where the annot parameter is set to true, and annot_kws is set with the size 20.

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Import seaborn as sns

import matplotlib.pyplot as plt

data = sns.load_dataset(“iris”)

sns-heatmap(data.corr(), annot=Truc, annot_kws={‘size’: 20})

plt.show()

Hence, the resultant heatmap plot is visualized with large numbers.

Conclusion

The article’s explanation of the Seaborn heatmap size ends here. To provide a graphical depiction of a matrix, a heatmap is employed. It employs different color hues for different values and draws a grid on the graph. We have shown the examples which defined the heatmap size with different approaches. However, the default plot size might not include a good data picture depicting a large matrix.



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